Robot learning has been applied to a wide range of challenging real world tasks, including dexterous manipulation [1]}, [2]}, legged locomotion [3]}, [4]}, and grasping [5]}, [6]}. It is less common, however, to see robotic learning applied to dynamic, high-acceleration, high-frequency tasks like precision table tennis (Figure REF ). Such settings put significant demands on a learning algorithm around safe exploration, accuracy, and sample efficiency. An outstanding question for robot learning is: can current techniques scale to meet the hard requirements of this setting?
<FIGURE>